Update pages/model.py

pages/model.py

@@ -1,60 +1,80 @@
+
 import streamlit as st
 import pickle
 import re
 import numpy as np
+import pandas as pd
+from io import StringIO
+from streamlit_lottie import st_lottie
+import json
 
 st.set_page_config(page_title="🔖 TagGPT - Auto Tag Your Questions", layout="centered")
 
-def clean_text(raw_text):
-    raw_text = re.sub(r"<.*?>", " ", raw_text)  # Remove HTML tags
-    raw_text = re.sub(r"[^a-zA-Z0-9\s]", " ", raw_text)  # Remove special characters
-    raw_text = re.sub(r"\s+", " ", raw_text.lower()).strip()  # Lowercase & strip spaces
-    return raw_text
+def load_lottie(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
 
 @st.cache_resource
+
 def load_assets():
-    try:
-        with open("model.pkl", "rb") as m:
-            tag_model = pickle.load(m)
-        with open("tfidf.pkl", "rb") as v:
-            text_vectorizer = pickle.load(v)
-        with open("mlb.pkl", "rb") as e:
-            tag_encoder = pickle.load(e)
-        return tag_model, text_vectorizer, tag_encoder
-    except Exception as error:
-        st.error(f"❌ Failed to load model components: {error}")
-        st.stop()
+    with open("model.pkl", "rb") as m:
+        tag_model = pickle.load(m)
+    with open("tfidf.pkl", "rb") as v:
+        text_vectorizer = pickle.load(v)
+    with open("mlb.pkl", "rb") as e:
+        tag_encoder = pickle.load(e)
+    return tag_model, text_vectorizer, tag_encoder
 
 tag_model, text_vectorizer, tag_encoder = load_assets()
 
-st.title("🎯 TagGPT - Smart Stack Overflow Tag Suggester")
-st.markdown("🚀 **Automatically generate relevant tags for your coding questions.**")
-st.markdown("Just enter your question title and description, and let AI do the tagging!")
+def clean_text(raw_text):
+    raw_text = re.sub(r"<.*?>", " ", raw_text)  # Remove HTML tags
+    raw_text = re.sub(r"[^a-zA-Z0-9\s]", " ", raw_text)  # Remove special characters
+    raw_text = re.sub(r"\s+", " ", raw_text.lower()).strip()
+    return raw_text
+
+st_lottie(load_lottie("tag_animation.json"), height=150, key="intro")  # Lottie animation (optional)
 
-question_title = st.text_input("🧠 Enter the **Question Title**")
-question_description = st.text_area("📝 Provide a **Detailed Description**", height=200)
-confidence_threshold = st.slider("📈 Select Tag Confidence Threshold", 0.1, 0.9, 0.3, 0.05)
+st.title("🏷️ TagGPT - Smart Stack Overflow Tag Suggester")
+st.markdown("**🚀 Instantly generate relevant tags for your coding questions using AI.**")
+
+q_title = st.text_input("🧠 Enter your question title")
+q_desc = st.text_area("📝 Describe your problem in detail", height=200)
 
 if st.button("🔍 Predict Tags"):
-    if not question_title.strip() or not question_description.strip():
-        st.warning("⚠️ Both title and description are required!")
+    if not q_title.strip() or not q_desc.strip():
+        st.warning("⚠️ Please provide both title and description.")
     else:
-        combined_input = clean_text(question_title + " " + question_description)
+        combined_input = clean_text(q_title + " " + q_desc)
         transformed_input = text_vectorizer.transform([combined_input])
 
         try:
             if hasattr(tag_model, "predict_proba"):
-                probs = tag_model.predict_proba(transformed_input)
-                tag_preds = (probs >= confidence_threshold).astype(int)
+                probs = tag_model.predict_proba(transformed_input)[0]
+                pred_tags = (probs >= 0.3).astype(int)  # Fixed threshold
             else:
-                tag_preds = tag_model.predict(transformed_input)
+                pred_tags = tag_model.predict(transformed_input)[0]
+                probs = pred_tags
 
-            final_tags = tag_encoder.inverse_transform(tag_preds)
+            tag_names = tag_encoder.classes_
+            selected_tags = [(tag, round(probs[i]*100, 2)) for i, tag in enumerate(tag_names) if pred_tags[i] == 1]
 
-            if final_tags and final_tags[0]:
-                st.success("🏷️ **Predicted Tags:**")
-                st.markdown("🔸 " + ", ".join(final_tags[0]))
+            if selected_tags:
+                st.success("🏷️ **Predicted Tags with Confidence:**")
+                for tag, score in selected_tags:
+                    st.markdown(f"**{tag}** — `{score}%`")
             else:
-                st.info("🤖 No tags predicted. Try adjusting the threshold or refining your input.")
+                st.info("🤖 No tags predicted. Try refining your input.")
+
+            # --- Download Option
+            tag_list = [tag for tag, _ in selected_tags]
+            df = pd.DataFrame({
+                "Title": [q_title],
+                "Description": [q_desc],
+                "Predicted Tags": [", ".join(tag_list)]
+            })
+            csv = df.to_csv(index=False).encode('utf-8')
+            st.download_button("📥 Download Tags as CSV", data=csv, file_name="tag_predictions.csv", mime='text/csv')
+
         except Exception as error:
             st.error(f"🚫 Prediction failed: {error}")